Knowledge that Transforms

We explore the effect of dealing patterns on consumer purchase behavior by developing a normative purchase quantity model that can incorporate any dealing pattern. The model adds to the stream of research on optimal purchasing policy by demonstrating how dealing patterns can be incorporated in a simple manner in dynamic programming models. Implications for purchase behavior are derived by employing the model in a numerical simulation in which time between deals is characterized by a Weibull distribution. The flexibility of the Weibull distribution enables us to establish how particular facets of the dealing distribution (e.g., certainty in deal timing, minimum time between deals) affect consumer behavior with respect to optimal purchase quantity, inventory, etc. One of the implications of the model is that the average quantity purchased on deal should be larger when there is greater certainty in deal timing. The model also shows that the average quantity purchased on deal should be larger when deals are spaced further apart. even though the buyer is presented with the same number of deals. We test certain model implications in a laboratory experiment and find actual behavior varying across dealing patterns in a manner consistent with model implications.

Economies of scale are evident when a firm's average costs decline while its output expands, as when an advertising agency raises its gross income by serving more accounts and/or larger accounts. Economies of scope appear when cost savings can be realized by a single agency producing several products jointly, as compared to many agencies each producing them separately. How important are economies of scale and scope in advertising agency operations? In this paper cost models are formulated which represent how the principal component of agency costs, employment level, varies according to the mix of media and services an agency provides and the total volume of advertising it produces. These models are estimated and tested cross-sectionally utilizing data pertaining to the domestic operations of 401 U.S. agencies for 1987. The empirical evidence reported here indicates that both scale and particularly scope economies are highly significant in the operations of U.S. advertising agencies. We find that of the 12,000 establishments comprising the industry in 1987, approximately 200–250 had domestic gross incomes of $3–4 million or more (or equivalently, billings of $20–27 million) and therefore had service mixes and operating levels sufficiently large to take full advantage of all available size-related efficiencies. Furthermore, the overall structure of the industry is one where these large, fully efficient firms created and produced more than half of all the national advertising utilized in the U.S. during 1987. At the same time, vast numbers of very small agencies appear to operate with substantial cost disadvantages compared to large firms as a consequence of these scale and scope economies. These findings carry important implications concerning possible future changes in the industry structure. It seems highly doubtful that scale economies could motivate further mergers among the largest 200–250 agencies. On the other hand, for small agencies, mergers and acquisitions might be attractive as means of mitigating their size-related cost disadvantages. Finally, our findings demonstrating the existence of scale and scope economies are consistent with the diminishing reliance on fixed rates of media commissions as the principal basis of agency compensation. They also cast strong doubts on size-related economies in operating costs as a viable explanation for the limited degree of vertical integration of agency services by large advertisers.

Among the more prominent truisms in marketing are 80/20 type laws, e.g., 20 percent of the customers account for 80 percent of the purchases. These kinds of statistics indicate a certain degree of concentration in customer purchases; i.e., the extent to which a large portion of the product's total purchases are made by a small fraction of all customers. Such concentration levels, suggesting that markets can be segmented in various ways, are often reported in basic marketing texts. We show that a meaningful interpretation of these concentration statistics is not nearly as easy or immediate as it is to compute them. The key factors influencing the degree of apparent concentration in purchases are reviewed, and we present a modeling approach for estimating the true level of relevant concentration among customers.

Based upon a recently developed multiattribute generalization of prospect theory's value function (Tversky and Kahneman 1991), we argue that consumer choice is influenced by the position of brands relative to multiattribute reference points, and that consumers weigh losses from a reference point more than equivalent sized gains (loss aversion). We sketch implications of this model for understanding brand choice. We develop a multinomial logit formulation of a reference-dependent choice model, calibrating it using scanner data. In addition to providing better fit in both estimation and forecast periods than a standard multinomial logit model, the model's coefficients demonstrate significant loss aversion, as hypothesized. We also discuss the implications of a reference-dependent view of consumer choice for modeling brand choice, demonstrate that loss aversion can account for asymmetric responses to changes in product characteristics, and examine other implications for competitive strategy.

Empirical research indicates that some consumers form price expectations which may impact their purchase behavior. While literature in operations research has built purchase policy models incorporating uncertain price expectations, these models have been built for commodities. Consumers face an environment with multiple brands. In this paper, we develop a model that incorporates consumer preferences and price expectations for multiple brands as determinants of normative consumer purchase behavior. The model demonstrates how commodity purchase policy models recognizing price uncertainty can be adapted to the study of multi-brand markets. The model is used to analyze the normative impact of changes in price promotion policies and holding costs on individual purchase behavior. It is also used in a Monte-Carlo market simulation that illustrates some scenarios where a post-promotion dip is more or less evident, and provides an explanation for the nonexistent post-promotion dip.

Relationships between buying and selling organizations in business markets are varied and complex. One important relationship is procurement, or the type of alliance that buyers form with sellers to fulfill their purchasing needs. Multiple sourcing is often proposed to prevent a variety of procurement problems. We develop a bidding model to investigate the effect of multiple sourcing on competitive behavior prior to supplier selection. The model treats the number of bidders (and the decision to bid) endogenously. This distinctive feature allows us to show that the distribution of the number of bids is not independent of the competition type. Most previous models have not considered this question at all. Although multiple sourcing does increase participation in a bidding competition, we show that it can lead to strategic pre-award price increases that can mitigate the effects of the advantages stressed by most previous analyses of post-award supplier management.

This paper looks at the comparative reliability of different methodological variants of the conjoint analysis procedure. It differs from previous studies in that it looks at three methods of data collection (Full Profile, Trade-off Matrices, and Paired Comparison) and two levels of a key attribute (price) across five different product categories. In addition it tests these manipulations using two different reliability assessment procedures. The results show that most manipulations have a significant effect on the reliability scores and many interaction terms are significant.

Brand positioning and brand pricing are important strategic decisions for marketing managers. Such decisions are interrelated and depend upon competitive brand positions and prices. However, any unilateral decisions may encourage repositioning and price adjustment by competitors thus leading to either new market equilibria or a price/positioning “war.” This paper examines such price and positioning decisions in a competitive environment by extending the ‘Defender’ consumer model to more complex equilibria. We assume that the brands are already in the market and that positions are “sticky” in the sense that brands cannot leapfrog one another. Among the insights for such incumbent brands are: • if prices are symmetric and held constant, firms seek to reposition toward the center of the market, • but price advantages by central firms can mediate that central tendency such that an equilibrium exists with positive profits; • if brand positions are held constant, the Nash equilibria for prices exist and are unique, and • category profits, the sum of brand profits, are maximized for maximum brand differentiation; • for two and three brand markets the (subgame perfect Nash) equilibrium, where positioning decisions are made with foresight on the price equilibria, causes firms to unilaterally seek maximum brand differentiation; but • for four or more brands greater foresight and/or cooperation appears necessary to reach the point of maximum brand differentiation. These results apply where the Defender consumer model is an exact description of consumer response and where the competitive reaction assumptions hold. In any empirical situation such forces might be softened resulting in differentiated, but not maximally differentiated, brands.

The effects of the marketing actions of one brand can be distributed among its competitors' market shares in a complex manner. This paper presents and illustrates methods for modeling brand competition and brand strategies in markets where competitive effects can be differentially and asymmetrically distributed. We discuss the empirical specification, parameter estimation and competitive—strategy implications of the models proposed. Price and advertising competition among eleven brands of an Australian household product is used to illustrate the application of these procedures.

Four algorithms for locating an “optimal” new product in a multiattribute product space—Albers and Brockhoff's PROPOPP; Gavish, Horsky, and Srikanth's Method IV; May and Sudharshan's PRODSRCH; and GRID SEARCH—are compared in terms of the relative share of preferences the new product will capture under different simulated market environments. These environments were both ones for which the algorithms were designed as well as other “more realistic” environments. Results indicate that algorithm performance is sensitive to the number of customers or segments, and the presence of probabilistic choice, and less sensitive to the numbers of existing products. Gavish, Horsky, and Srikanth IV (GHS IV) and PROPOPP performed best under the market conditions for which they were designed and GHS IV proved quite robust under variation from these conditions. PROPOPP's performance deteriorated, however, in large sample size problems (n ≥ 200). PRODSRCH (a general purpose optimizer) was inferior under these special market conditions, but superior under other more general ones.